Determining offer terms from text

ABSTRACT

Systems, methods, and machine readable and executable instructions are provided for determining offer terms from text. A method for determining offer terms from text can include mapping keywords to a domain of a procurement event, and receiving, to a computing device, an offer text associated with the procurement event. Event-specific entities are identified, by the computing device, in the offer text. The computing device determines the domain of the procurement event from the identified event-specific entities, and using the mapped keywords corresponding to the determined domain, determines offer components from the offer text, extracts offer parameters from the offer text, and constructs the offer structure using the identified event-specific entities, derived offer components, and extracted offer parameters.

BACKGROUND

An entity, such as a business or an individual, may receive writtencommunication concerning an offer to sell an item from a vendor. Thewritten communication may include one or more messages that containtext. However, the text might not be arranged to conform to anyparticular (e.g., pre-defined) offer structure or format. An individualmessage may only describe a portion of the offer, and/or may or may not,explicitly refer to other prior, messages, documents, or conduct. Thewritten communication may include a description of the item and/or theeconomic terms of an offer at which the vendor is willing to sell theitem. The written communication may be electronically or physicallycommunicated. For all these reasons, among others, it can be difficultto automatically ascertain, record, and/or compare terms of an offerusing a computing device. One previous approach includes merely savingthe entirety of written communications concerning the offer terms forfuture review and analysis by a person,

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example process of determining offer terms fromtext according to the present disclosure.

FIG. 2 is a flow chart illustrating an example of a method fordetermining offer terms from text according to the present disclosure.

FIG. 3 illustrates a block diagram of an example of a computing systemfor determining offer terms from text according to the presentdisclosure.

DETAILED DESCRIPTION

Embodiments of the present disclosure may include methods, systems, andmachine readable and executable instructions and/or logic. An examplemethod for determining offer terms from text can include mappingkeywords to a domain of a procurement event, and receiving, to acomputing device, an offer text associated with the procurement event.Event-specific entities are identified, by the computing device, in theoffer text. The computing device determines the domain of theprocurement event from the identified event-specific entities, and usingthe mapped keywords corresponding to the determined domain, determinesoffer components from the offer text, extracts offer parameters from theoffer text, and constructs the offer structure using the identifiedevent-specific entities, derived offer components, and extracted offerparameters.

The main problem addressed by the present disclosure is the automateddetermination of offer terms, such as discount offers, from varioustypes of communication media containing semi- and/or unstructured textformat, and converting those offer terms into a structured format (e.g.,pre-defined, standardized). The structured format can then be used forstoring the offer terms, for example, inside a database or anotherconvenient file type so that the offer terms can be easily obtained andprocessed by various software applications.

During procurement events, such as in response to an RFQ (Request forQuote), the terms of an offer from a particular supplier may becommunicated using one or more messages (e.g., letters, e-mails, faxes,voice, etc.), which may be in free text format. These variouscommunications may only be individually understandable within anunderstood context within which the messages occur. However, the contextmay not be explicitly stated within any of the messages, and thusdifficult for a machine to ascertain. For example, an RFQ may providethe standard terms of a purchase sought by a buyer. The offer termssought by the buyer may refer to other documents or transactions orconduct (e.g., same terms as the last sale) defining default offerterms. As used herein, offer terms refer to offer components and offerparameters corresponding to respective components. An RFQ may besubmitted to several suppliers in order to provoke competitive offersfor sale. It may be desirable for a buyer (or seller) to be able tocompare offer terms. In order to facilitate processing by decisionsupport, data analysis, and reporting systems, among others, convertingthe terms of an offer (e.g., from various multiple vendors) into acommon structured format can therefore be beneficial.

According to various embodiments, determining offer terms from offertext according to a structured offer format can be accomplished byinitially identifying event-specific entities (e.g., nouns) in the offertext using domain-specific definitions, dictionary, and/or ontology. Asused herein, “offer text” can include, in the aggregate, any textassociated with a particular offer and/or text referred to incommunications concerning a present offer. Thus, the offer text caninclude messages and documents from a seller, messages and documentsfrom a buyer, and/or other applicable documents (e.g., third partydocuments). Messages can include e-mail and/or verbal communicationsthat have been transcribed and memorialized into written form such as bya voice recognition process, for example. Documents can includeelectronic representations of physical print media orelectronically-stored text.

Event-specific entities may be particular vendors, types of equipment(e.g., drives), and/or individual item identities (e.g. part numbers),among others. The domain may be a particular technology or topic, suchas a particular RFQ or other procurement event. According to embodimentsof the present disclosure, a domain can provide context to a dictionaryor ontology used in ascertaining the meaning of offer text. For example,the text “spam” may have a very different meaning with respect to thedomain of electronic communications versus its meaning with respect tothe domain of restaurant operations. A domain may be explicitly provided(e.g., communications labeled as being associated with a particularprocurement event or indicated via metadata upon storage thereof) orascertained by analysis of portions of the offer text.

As domain and event-specific entities are ascertained, components of aparticular offer structure can be determined in the offer text. Asfurther discussed below, offers can be structured in a number ofdifferent ways. However, different offer structures can each havecertain associated components (e.g., base price and discount therefrom).Once the structure of the offer is determined from the offer text,parameters (e.g., values) corresponding to the components of thedetermined offer structure can be identified in the offer text andextracted therefrom for storage in a more structured format.

Offer terms may be presented having various structures and/or at varyinglevels of explicitness. While a human may understand a context for aparticular offer based on their cumulative education, experience, and/orone or more prior communications that can serve to define terms of anoffer in some way (e.g., place the most-recent communications in acontext), a computing device receiving offer text may not be able to“connect the dots” in the same manner to derive offer terms from semi-or unstructured offer text. According to the present disclosure, semi-or unstructured offer text can be converted into a well-defined offerstructure by incorporating event level knowledge, domain information,and text (e.g., word, phrase) meaning from a domain-specific dictionaryor ontology. The methods of the present disclosure go beyond the rote(e.g., non-contextual) information extraction, natural languageprocessing, and/or text mining methods of previous approaches.

Once the offer structure is determined and parameters corresponding tothe components of the determined offer structure are identified, theoffer can then be constructed in a structured format using theidentified entities, offer components, and corresponding parameters. Theconstructed offer can be stored according to a preferred format in adatabase, or other convenient file format, for future retrieval and/oruse.

In the following detailed description of the present disclosure,reference is made to the accompanying drawings that form a part hereof,and in which is shown by way of illustration how examples of thedisclosure may be practiced. These examples are described in sufficientdetail to enable those of ordinary skill in the art to practice theembodiments of this disclosure, and it is to be understood that otherexamples may be utilized and that process, electrical, and/or structuralchanges may be made without departing from the scope of the presentdisclosure. As used herein, the designators “N,” “M,” and “X”particularly with respect to reference numerals in the drawings,indicate that a number of the particular feature so designated can beincluded with examples of the present disclosure. The designators canrepresent the same or different numbers of the particular features.

The figures herein follow a numbering convention in which the firstdigit or digits correspond to the drawing figure number and theremaining digits identify an element or component in the drawing.Similar elements or components between different figures may beidentified by the use of similar digits. For example, 116 may referenceelement “16” in FIG. 1, and a similar element may be referenced as 216in FIG. 2. Elements shown in the various figures herein can be added,exchanged, and/or eliminated so as to provide a number of additionalexamples of the present disclosure. In addition, the proportion and therelative scale of the elements provided in the figures are intended toillustrate the examples of the present disclosure, and should not betaken in a limiting sense.

An offer to sell, or modify terms of a sale, a product or service may becaptured as an offer communicated by multiple communications (e.g.,messages) from a supplier. Individual messages may not set forth theentire set of the offer terms. That is, an individual message mayprovide a portion of the offer terms. Furthermore, individual messagesmay affirm, reject, and/or modify one or more terms set forth inprevious communications, prior agreements, prior dealings, and/orstandard industry practices. Additionally, multiple communications takentogether may not explicitly set forth all applicable offer terms relatedto a particular transaction. Frequently, portions of an offer that arenot different than a previous offer, standard terms, industry standardpractice, or other understood terms are not explicitly addressed inspecific offer statements. For example, statutes codifying the UniformCommercial Code, common law, conduct of the parties and/or verbalcommunications may be the source for offer terms missing from writtencommunications concerning a particular transaction.

Offer communications are often formatted in semi- or un-structured text(e.g., free text) that make it challenging for a buyer (e.g., a person)to compare offers with one another. As computing devices are used moreand more in communications, processing, inventory, accounting and otheraspects of a business entity's purchasing function, it can be beneficialto convert offers communicated in free text into a structured formatthat can be used for storing the offer details inside a database orother convenient file type so that the offer details may be read,processed, and/or stored by various software applications.

Another difficulty is that offers may be of different types, which makeit difficult for the offers to be directly compared to one another. Forexample, one supplier may arrange an offer structure of a particulartype that is used to respond to solicitations from prospectivecustomers. When a prospective customer solicits offers from a number ofsuppliers, the customer may receive back offers of different types,making direct comparison between offers difficult. Alternatively, aprospective customer may solicit an offer from a particular supplier,and request that the supplier provide an offer of a specific type.However, the supplier may not normally provide offers of this type, andthus has to convert its standard type offer to the type being requested.

One type of offer that is referred to herein is an incremental offer. Anincremental offer includes a maximum base price for an item, and one ormore price discounts having one or more corresponding volume thresholds.There may be equal numbers of price discounts and volume thresholds.When the party purchases a volume of the item that exceeds a givenvolume threshold, the maximum base price is decreased for the volume ofthe item above the threshold by a corresponding price discount, but themaximum base price is not decreased for the volume below the threshold.For example, an incremental offer may be specified by a maximum baseprice, one or more price discounts, and one or more volume thresholdsthat correspond to the price discounts.

The price discounts may be positive (as described above) or negative(e.g., additional items are more expensive per unit). In the case wherea price discount is negative, it is effectively a surcharge or mark-up.For example, between zero and fifty units, a supplier may charge a baseprice of five dollars. Above fifty units, a supplier may specify asurcharge or mark-up of one dollar, such that the units sold in excessof fifty units cost six dollars each. In this case, the one-dollarsurcharge or mark-up is considered a price discount of negative onedollar.

The same pricing terms provided by a positive discount (e.g., a greaterquantity of items cost less per item) could also be alternativelyphrased as a particular minimum base price (e.g., associated with eachitem based on a certain volume of items) and one or more price addersassociated with one or more corresponding lower volume thresholds. Forexample, an offer may provide a minimum price for ordering more than athreshold quantity of items, and provide an additional per unit cost forordering less than the threshold quantity of items.

Terms other than price changes may also be incrementally adjusted. Forexample, free shipping, gift wrapping, or other non-price offer termsmay change based on quantity or other sale attribute (e.g., color, size,delivery time, type of payment, etc.).

Another type of offer is referred to herein as a cumulative offer. Acumulative offer also includes a base price for an item, and one or moreprice discounts having one or more corresponding volume thresholds, suchthat there are equal numbers of price discounts and volume thresholds.When the party purchases a volume of the item that exceeds a givenvolume threshold, the base price for the entire volume is reduced by acorresponding price discount. That is, the base price for the volumes ofthe item both below and above the threshold is reduced by the pricediscount. As such, thresholds are used just to determine the unit pricethat applies to the whole purchase.

For example, a cumulative offer may also be specified by a base price,one or more price discounts, and one or more volume thresholds thatcorrespond to the price discounts. However, the discounts apply to thewhole volume, rather than only the portion of the volume that is above aparticular threshold. As with incremental offers, cumulative offers mayhave price discounts that are positive or negative (e.g., the pricediscounts are effectively price surcharges), may be described as addersfor lower volumes, and/or may involve non-price offer terms.

Embodiments of the present disclosure provide for determining offerterms from text. Further embodiments of the present disclosure providefor storing values quantifying the offer terms according to apre-defined set of possible offer terms. As such, disparate types ofoffers and/or offer term communication formats that are received fromdifferent suppliers can be stored in a convenient (e.g., standardized)format, compared to one another, and/or further utilized by softwareoperating on one or more computing devices.

FIG. 1 illustrates an example process of determining offer terms fromtext according to the present disclosure. An offer structure usuallycontains and offer statement and one or more conditions. For simplicity,the offer structure for a discount type of offer is used to illustratethe features of the present disclosure. However, embodiments are notlimited to any particular offer structure in the offer text, and themethodology discussed below can be applied to other offer types such asmarkups, price adjustments, etc.

A discount offer structure can contain one or more of the followingcomponents: base price, value of discount, discount value measurementtype (e.g., absolute, relative), item(s) to which the discount isapplicable, seller offering the discount, and exclusions.

FIG. 1 illustrates an example process of determining offer terms fromtext according to the present disclosure. Shown in FIG. 1 are a numberof seller messages, 136-1, . . . , 136-X, and a number of buyermessages, 138-1, . . . , 138-Y. The respective messages, comprisingoffer text associated with a particular procurement event are sent fromor received to a computing device (e.g., the buyer's computing device).The seller messages may have been generated in response to an RFQ, orother (e.g., less-formal) purchase inquiry by the buyer, or may be partof an unsolicited offer for sale initiated by the seller. One or more ofthe seller messages may explicitly or impliedly be based on one or moreprevious understandings between the seller and buyer, including but notlimited to prior agreements 130, prior dealings 132 (e.g., previoustransactions of similar goods), and/or standard industry practices 134,each of which may be memorialized in some form of text-baseddocumentation. For example, default terms set forth in an RFQ solicitingseller bids can be a previous understanding. Therefore, the text of theRFQ may constitute part of the offer text upon which subsequent sellermessages modify and/or supplement. Arrows 150 indicate that the prioragreements 130, prior dealings 132, and/or standard industry practices134 can be incorporated by explicit and/or implicit reference into buyermessages 138-1, . . . , 138-Y and/or provide context thereto. Arrows 152indicate that the prior agreements 130, prior dealings 132, and/orstandard industry practices 134 can be incorporated by explicit and/orimplicit reference into seller messages 136-1, . . . , 136-X and/orprovide context thereto.

An initial seller message 136-1 (e.g., “seller message 1”) associatedwith a particular procurement event may build upon one or more of theprevious understandings, such as the RFQ. The initial seller message136-1 represents in FIG. 1 an e-mail message that contains the followingfree text, “Supplier-S would be happy to offer 3% price reduction onSCSI drives after 250K.”

The initial seller message 136-1 may trigger a response, such as furtherinquiry, from buyer, for example, via buyer message 1 138-1. Buyermessage 1 may refer to, or modify, or otherwise build upon the initialseller message, as indicated by arrow 146. The buyer message(s) andadditional seller message(s) may change and/or supplement the contentsof seller message 1, and thus contribute to the offer text. Thesesubsequent messages may refer, directly or indirectly, to previousmessages from the other party, as indicated by arrows 147 and 148. Thefollowing example will limit the following discussion of offer textanalysis to illustrate the present method using only the contents ofseller message 1. However, it should be understood that the offer textcan include the additional buyer and seller messages, as well as theprior agreements 130, prior dealings 132, and/or standard industrypractices 134, and other relevant documents (e.g., verbal conversationsrecorded and transcribed to text format), among others.

A previous approach utilizing general purpose text extraction or textmining might have a lot of difficulty in understanding the meaning ofthe offer statement contained in seller message 1 as an offer. Naturallanguage processing approach may not be able to do much without domainspecific knowledge. The approach of the present disclosure firstincludes identification of event-specific entities. According to someembodiments of the present disclosure, the particular event isestablished from one or more of the entities identified within the offertext, with or without, reference to additional information. For example,an RFQ concerning a particular event may have been directed toSupplier-S. Thus, identification of Supplier-S may be indicative of theparticular event.

However, Supplier-S may have been invited to bid on numerous RFQs, onlya portion of which concern SCSI drives. Thus, identification of“Supplier-S” may identify a subset of procurement events, andidentification of “SCSI drives” in the offer text may further identify aparticular one of the subset of procurement events to which sellermessage 1 pertains.

According to other embodiments of the present disclosure, the event maybe explicitly stated in the communications. For example, seller message1 may explicitly reference a particular procurement event (e.g., in themessage subject line) or a code mapped to a particular procurementevent. Other methods for associating a particular message to aparticular event are also contemplated by the present method. Forexample, the destination address of seller message 1 may be mapped aparticular event. By one of the above-mentioned approaches, or by otheranalysis, a computing device can determine a domain of a procurementevent to which a particular message pertains. Once a domain isdetermined, the computing device can utilize meanings of keywords thathave previously been mapped to the domain in further analyzing the offertext.

In situations where the content or the subject line of a specificmessage does not allow direct identification of the event to which themessage pertains, additional metadata (such as subject line content,timestamps, and/or recipient lists) can be used to construct a hierarchy(e.g., order, tree, etc.) of messages that can be used sequentially toeliminate the ambiguity about the contextual event. For example, amessage from Supplier A (e.g., Message 1) with content “$5” does notcontain much usable information. Knowing that this particular message isin reply to a previous message (e.g., Message 2) with content “How aboutItem X?” adds some context to Message 1; however, the contents of thetwo messages together still leaves substantial ambiguity. If therelative location of these two messages in a hierarchy of messagethreads can be identified, the fact that these two messages are precededby Message 3 with content “$3” and Message 4 with content “What is theinvoice price of Item Y in your offer?” allows construction of twoimportant pieces of information: 1) Supplier A's invoice price offer forItem Y is $3, and 2) Supplier A's invoice price offer for Item X is $5.The growing corpus of information that is obtained by going up thehierarchy of the messages reduces the number of candidate events towhich a particular message may pertain.

As discussed above, identifying, by the computing device, event-specificentities in the offer text can lead to determination of the domain of aprocurement event. Or, establishing a particular event to which thepresent message pertains, either directly or indirectly, may lead tofurther identification of entities in the offer text. The determinationof event-specific entities and/or domain of a procurement event may bean iterative process within one message, or as applied to multiplemessages comprising the offer text. Identification of event-specificentities is more useful in understanding the overall offer statement andits meaning, as opposed to using only domain specific dictionary orontology. A domain specific ontology can help an information extractionapproach identify certain keywords, such as “Hard Disk”, “SCCI”,“Memory”, “Flash”, etc. However, event specific entities can be muchmore useful in building a specific offer structure out of a free textstatement that is received from a supplier. For example, the keyword“SCSI” actually refers to a specific subset of items (part numbers) thatare being purchased in a particular procurement event.

Therefore, the proposed method maps that keyword to a subset of items inthe current event rather than simply matching it with other relatedkeywords inside a dictionary or ontology. The mapping of keywords intoevent specific entities provides deeper insight into their actualmeaning within the specific procurement event.

Identifying event-specific entities in the offer text can includemapping identified entities to a particular offer component and/ordetermining that a noun within the offer text is an entity. In theexample illustrated in FIG. 1, the entity “Supplier-S” refers to thesupplier that submits the offer. Thus, “Supplier-S” can be mapped to adatabase having at least a field 142 and value 144 corresponding to thefield as shown in FIG. 1.

The entity “SCSI” can identify a domain of the procurement eventapplicable to the example illustrated in FIG. 1. Thus, “SCSI” can bemapped to the database corresponding to the domain as shown at 154. Thatis, the first level of context for analysis of the offer text is thatrelated to SCSI drives, thus keywords mapped to the SCSI domain can beused as a first level in determining the meaning of the offer text.

The second level is the particular event (e.g., procurement event). Thatis, keyword meanings can be further defined according to their meaningwith respect to a particular procurement event. The entity “SCSI” canalso identify a subset of items (e.g., part numbers) known to be out forprocurement that are associated with SCSI interfaces (e.g., thoseassociated with SCSI interfaces). Thus, the particular procurement eventcan be identified, from which event-specific keyword definitions can besubsequently used.

For example, if one of several pending procurement events (e.g., RFQ)have items related to SCSI interfaces, presence of the word “SCSI” inthe offer text can also identify the particular procurement event andthe particular items (e.g., item-1 and item-2 set forth in the RFQ butnot explicitly mentioned in seller message 1) that use SCSI interfaces.Thus, item-1 and item-2 can be identified indirectly as being an entityto which the offer text (e.g., seller message) concerns and cause theidentified items to be mapped to the database as indicated at 158.

Item-1 and Item-2 may, for example, be derived from prior dealings(e.g., and RFQ) in association with the SCSI interfaces determination ofdomain made from seller message 1. Items that are being purchased areusually available either in the event specific data, or throughcompany-wide item databases as parameters of particular items. FIG. 1illustrates a particular domain- and/or event-specific dictionary 156and/or onotology 160 being used to determine meaning of the textportions of seller message 1 and influencing the mapping of keywords andinformation determined from the keywords to the entries in the databaseassociated with the offer.

Next, the components of the offer structure are identified. This can bemuch more difficult than identification of entities. The offer structurecontains one or more conditions and an offer statement. A discount offertype is used in the example illustrated in FIG. 1; however, embodimentsof the present disclosure are not limited to any particular offer typeand may be applied to other offer types such as markups, priceadjustments, and the like. A discount offer structure can contain thefollowing components: a value of the discount; a measurement type ofdiscount value (e.g., absolute or relative); a set of items (e.g., partnumbers) to which the discount applies; a supplier that is offering thediscount; and a set of pre-defined alternative discounts that cannot beapplied at the same time. Additional parameters of an offer may includeterms related to general payment terms. For example, an offer mayspecify the due dates for invoiced payments. Similarly, a rebate offermay specify explicit dates at which the rebate will be paid. Other offerstructures may have different components.

A discount condition structure can include the following components: aset of entities that provide references to items, such as part numbers,that are relevant in the current context; a condition metric (e.g.,spend, volume, etc.); a condition scope that explains how the conditionshould be applied on individual items and/or suppliers (e.g., for each,for all, etc.); a threshold value, a threshold comparison operator(e.g., at least, at most, equals, etc.); and a supplier.

For example with respect to seller message 1 in FIG. 1, “pricereduction” indicates that the offer is a discount type of offer. Theword “after” is a condition threshold operator. It is necessary tofigure out that the word “after” is referring to an “at least”comparison operator based on its relative location in the offerstatement. The method of the present disclosure can use a domainspecific ontology to interpret such words in the offer statement.

According to the present disclosure, the parameters are subsequentlyidentified. For example with respect to FIG. 1, “3%” is the discountvalue as a “relative” measurement, and “250K” is a condition value as“absolute” measurement.

Next, the offer structure can be constructed and mapped to the databaseas shown. The discount value of 3 can be extracted from the offer text.A discount measurement is determined to be “relative” based upon thepercentage sign in the offer text associated with the discount value.The set of items to which the discount applies, item-1 and item-2, havebeen determined, as discussed above, as has the supplier that isoffering the discount (e.g., Supplier-S).

In the example illustrated in FIG. 1, the set of items on which thecondition applies is item-1 and item-2 (as discussed above). Thecondition metric is volume, determined from the presence of a quantity.The condition scope is “for all.” Although not specified explicitly,relative discounts can be assumed to have this scope since that is thedefault scope in the procurement events for hard disk drives. Thethreshold value is 250,000, which must be ascertained from the meaningof the term “250K.” The threshold comparison operator is “at least”determined from the term “after” as previously discussed. And thesupplier for which the given condition applies is Supplier-S, also aspreviously discussed. From the information available from the offertext, there is only one condition defined by the above-mentioncomponents.

As shown by the arrows mapping portions of offer text from sellermessage 1 to the value 144 in the database, the extracted offerstructure can be stored in one or more database table(s), or in a filetype (e.g., spreadsheet, XML file) that is suitable for storingstructured data. Thereafter, details of a particular offer stored insuch a well-defined format can easily be communicated into othersoftware applications for further processing (e.g., comparison, economicevaluation, purchasing, etc.) The structured data can have some, all, orfewer than those fields shown in FIG. 1 and associated value entries.

FIG. 2 is a flow chart illustrating an example of a method fordetermining offer terms from text according to an example of the presentdisclosure. The method includes mapping keywords to a domain of aprocurement event 270 and receiving, to a computing device, an offertext associated with the procurement event 272. the method furtherincludes identifying, by the computing device, event-specific entitiesin the offer text 274, and determining, by the computing device, thedomain of the procurement event from the identified event-specificentities 276. The method also includes using the mapped keywordscorresponding to the determined domain 278 and determining, by thecomputing device, offer components from the offer text 278A. Offerparameters are extracted by the computing device from the offer text278B, and the offer structure is constructed by the computing deviceusing the identified event-specific entities, derived offer components,and extracted offer parameters. Offer parameters can be numerical ortextual, and can concern economic, legal, technical, and/or other termsthat define an offer and/or the terms of acceptance of an offer,including conditional and prerequisite terms. Offer components andparameters may be a combination of expressly stated, implied, and/ordefault terms (e.g., defined by statute, regulation, rule, case law,prior dealings, business practices, etc. if not modified by the partiesto an agreement). Offer components can include price, quantity, and/oridentification of the subject matter to which the price and quantityapply, and offer parameters are the particular “value” corresponding torespective offer component, where “value” can be a number or text. Forexample, offer components can be the fields 142 shown in FIG. 1 andoffer parameters can be the values 144 shown in FIG. 1 corresponding tothe respective fields 142.

FIG. 3 illustrates a block diagram of an example of a computing systemfor determining offer terms from text according to the presentdisclosure. The computing system 300 shown in FIG. 3 is a networkedcomputing system. The communication network 306 can be a private networksuch as a local area network or wide area network, or can be a publicnetwork, such as the Internet. However, examples of the presentdisclosure are not limited to a particular computing systemconfiguration.

The computing system 300 can be comprised of a number of computingresources communicatively coupled to the network 306. FIG. 3 shows afirst (e.g., a seller's) computing device 302 that may also have anassociated data source 304, and one or more input/output devices 303(e.g., keyboard, electronic display). A second (e.g., a buyer's)computing device 308 is also shown in FIG. 3 being communicativelycoupled to the network 306, such that one or more messages 322-1, . . .322-N, may be communicated through the network between the first andsecond computing devices.

Computing device 308 may include one or more processors 310communicatively coupled to a non-transitory computer-readable medium312. The non-transitory computer-readable medium 312 may be structuredto store executable instructions 314 (e.g., one or more programs) thatcan be executed by the one or more processors 110 and/or data. Thesecond computing device 308 may be further communicatively coupled to aproduction device 316 (e.g., electronic display, printer, etc.). Secondcomputing device 308 can also be communicatively coupled to an externalcomputer-readable memory 318.

The second computing device 308 can cause an output to the productiondevice 316, for example, as a result of executing instructions of one ormore programs stored non-transitory computer-readable medium 312, by theat least one processor 310, to implement a method of determining offerterms from text according to the present disclosure. Causing an outputcan include, but is not limited to, displaying text and images to anelectronic display and/or printing text and images to a tangible medium(e.g., paper). For example, an offer may be determined from textcomprising one or more messages from a seller. Terms of the offer may bedetermined by the second computing device 308, stored in a database suchas may be maintained in external computer-readable memory 318, output toproduction device 316, and/or printed to a tangible medium.

First 302 and second 308 computing devices are communicatively coupledto one another through the network 306. While the computing system isshown in FIG. 3 as having only two computing devices, the computingsystem can be comprised of additional multiple interconnected computingdevices, such as servers and clients. Each computing device can includecontrol circuitry such as a processor, a state machine, applicationspecific integrated circuit (ASIC), controller, and/or similar machine.As used herein, the indefinite articles “a” and/or “an” can indicate oneor more than one of the named object. Thus, for example, “a processor”can include one processor or more than one processor, such as a parallelprocessing arrangement.

The control circuitry can have a structure that provides a givenfunctionality, and/or execute computer-readable instructions that arestored on a non-transitory computer-readable medium (e.g., 312, 318).The non-transitory computer-readable medium can be integral (e.g., 312),or communicatively coupled (e.g., 318), to the computing device (e.g.308), in either in a wired or wireless manner. For example, thenon-transitory computer-readable medium can be an internal memory, aportable memory, a portable disk, or a memory located internal toanother computing resource (e.g., enabling the computer-readableinstructions to be downloaded over the Internet). The non-transitorycomputer-readable medium 330 can have computer-readable instructionsstored thereon that are executed by the control circuitry (e.g.,processor) to provide a particular functionality.

The non-transitory computer-readable medium, as used herein, can includevolatile and/or non-volatile memory. Volatile memory can include memorythat depends upon power to store information, such as various types ofdynamic random access memory (DRAM), among others. Non-volatile memorycan include memory that does not depend upon power to store information.Examples of non-volatile memory can include solid state media such asflash memory, EEPROM, phase change random access memory (PCRAM), amongothers. The non-transitory computer-readable medium can include opticaldiscs, digital video discs (DVD), high definition digital versatilediscs (HD DVD), compact discs (CD), laser discs, and magnetic media suchas tape drives, floppy discs, and hard drives, solid state media such asflash memory, EEPROM, phase change random access memory (PCRAM), as wellas other types of machine-readable media.

The above specification, examples and data provide a description of themethod and applications, and use of the system and method of the presentdisclosure. Since many examples can be made without departing from thespirit and scope of the system and method of the present disclosure,this specification merely sets forth some of the many possibleembodiment configurations and implementations.

Although specific examples have been illustrated and described herein,those of ordinary skill in the art will appreciate that an arrangementcalculated to achieve the same results can be substituted for thespecific examples shown. This disclosure is intended to coveradaptations or variations of one or more examples of the presentdisclosure. It is to be understood that the above description has beenmade in an illustrative fashion, and not a restrictive one. Combinationof the above examples, and other examples not specifically describedherein will be apparent to those of skill in the art upon reviewing theabove description. The scope of the one or more examples of the presentdisclosure includes other applications in which the above structures andmethods are used. Therefore, the scope of one or more examples of thepresent disclosure should be determined with reference to the appendedclaims, along with the full range of equivalents to which such claimsare entitled.

Various examples of the system and method for collaborative informationservices have been described in detail with reference to the drawings,where like reference numerals represent like parts and assembliesthroughout the several views. Reference to various examples does notlimit the scope of the system and method for displaying advertisements,which is limited just by the scope of the claims attached hereto.Additionally, any examples set forth in this specification are notintended to be limiting and merely set forth some of the many possibleexamples for the claimed system and method for collaborative informationservices.

Throughout the specification and claims, the meanings identified belowdo not necessarily limit the terms, but merely provide illustrativeexamples for the terms. The meaning of “a,” “an,” and “the” includesplural reference, and the meaning of “in” includes “in” and “on.” Thephrase “in an embodiment,” as used herein does not necessarily refer tothe same embodiment, although it may.

In the foregoing Detailed Description, some features are groupedtogether in a single embodiment for the purpose of streamlining thedisclosure. This method of disclosure is not to be interpreted asreflecting an intention that the disclosed examples of the presentdisclosure have to use more features than are expressly recited in eachclaim. Rather, as the following claims reflect, inventive subject matterlies in less than all features of a single disclosed embodiment. Thus,the following claims are hereby incorporated into the DetailedDescription, with each claim standing on its own as a separateembodiment.

What is claimed:
 1. A method for determining offer terms from text, comprising: mapping keywords from a procurement event to a domain of the procurement event; receiving, to a computing device, an offer text associated with the procurement event; identifying, by the computing device, event-specific entities in the offer text; determining, by the computing device, the domain of the procurement event from the identified event-specific entities; and using the mapped keywords corresponding to the determined domain: determining, by the computing device, offer components from the offer text; extracting, by the computing device, offer parameters from the offer text; and constructing, by a computing device, an offer structure using the identified event-specific entities, derived offer components, and extracted offer parameters.
 2. The method of claim 1, wherein identifying the event-specific entities includes: relating communications of the offer text into a hierarchy based on content and metadata of the communications; and interpreting the content of the communications in the context of the hierarchy.
 3. The method of claim 1, wherein determining the offer components includes selecting a discount type based on economic keywords of the text.
 4. The method of claim 3, wherein determining the offer components includes identifying and interpreting relational portions of the text offer using domain-specific references.
 5. The method of claim 4, wherein extracting offer parameters includes associating a numerical portion of the text offer with an item quantity or pricing term based on relative positioning of the numerical portion with respect to an economic keyword or item keyword.
 6. The method of claim 1, further comprising storing the identified event-specific entities, derived offer components, and extracted offer parameters in a database configured to have offer structure fields and corresponding variable values.
 7. The method of claim 1, wherein keyword mapping utilizes domain-specific definitions, dictionary, or ontology.
 8. The method of claim 1, wherein the offer parameters comprise a base price, one or more price discounts, and one or more volume thresholds indicating volumes at which the price discounts are to be applied.
 9. The method of claim 1, wherein the offer structure is a discount condition structure that includes a condition metric, a condition scope, a threshold value, a threshold comparison operator, a set of entities that provide references to items to which the discount condition structure applies, and a supplier offering the discount.
 10. The method of claim 9 wherein the offer text includes at least one document not communicated in the multiple messages that is selected from a group comprising: a standing agreement, prior dealings, and standard industry practices.
 11. The method of claim 1, wherein determining the offer components includes deriving from the offer text a condition metric, condition scope, threshold value, a threshold comparison operator, and at least one item to which the condition metric, condition scope, threshold value, and threshold comparison operator apply.
 12. A non-transitory computer-readable medium having computer-readable instructions stored thereon that, if executed by one or more processors, cause the one or more processors to: map keywords from a procurement event to a domain of the procurement event; receive an offer text associated with the procurement event; identify event-specific entities in the offer text; determine the domain of the procurement event from the identified event-specific entities; and using the mapped keywords corresponding to the determined domain: determine offer components from the offer text; extract offer parameters from the offer text; and construct an offer structure using the identified event-specific entities, derived offer components, and extracted offer parameters.
 13. The non-transitory computer-readable medium of claim 12, wherein constructing the offer structure includes a value of discount, a measurement of discount value, a set of items to which the discount is applicable, a supplier offering the discount, and at least one alternative discount.
 14. The non-transitory computer-readable medium of claim 12, further including computer-readable instructions stored thereon that are executed by the one or more processors to derive from the offer text a condition metric, condition scope, threshold value, a threshold comparison operator, and at least one item to which the condition metric, condition scope, threshold value, and threshold comparison operator apply.
 15. The non-transitory computer-readable medium of claim 12, wherein the non-transitory computer-readable medium is communicatively coupled to the one or more processors via a communication network.
 16. The non-transitory computer-readable medium of claim 12, further including computer-readable instructions stored thereon that are executed by the one or more processors to store an offer parameter in a database as a value associated with a field that corresponds to an offer component.
 17. The non-transitory computer-readable medium of claim 12, further including computer-readable instructions stored thereon that are executed by the one or more processors to: receive the offer text in multiple electronic messages; relate the multiple electronic messages into a chronological order based on content and metadata of the communications; and interpret the content of a particular electronic message based on the content of at least one electronic message previously occurring in the chronological order to the particular electronic message.
 18. A computing system, comprising: A computing device having at least one processor; a production device communicatively coupled to the computing device; and a non-transitory computer-readable medium having computer-readable instructions stored thereon that, if executed by the at least one processor, cause the at least one processor to: map keywords from a procurement event to a domain of the procurement event; receive an offer text associated with the procurement event; identify event-specific entities in the offer text; determine the domain of the procurement event from the identified event-specific entities; and using the mapped keywords corresponding to the determined domain: determine offer components from the offer text; extract offer parameters from the offer text; and construct an offer structure using the identified event-specific entities, derived offer components, and extracted offer parameters.
 19. The computing system of claim 18, wherein the non-transitory computer-readable medium is communicatively coupled to the at least one processor via the Internet.
 20. The computing system of claim 18, wherein the non-transitory computer-readable medium having computer-readable instructions stored thereon that, if executed by the at least one processor, cause an output to the production device that includes the offer components and offer parameters. 